约束行为克隆用于机器人学习
Constrained Behavior Cloning for Robotic Learning
AI总结：
本文提出几何与历史约束的行为克隆（GHCBC），结合高层状态信息，通过几何约束姿态预测和时间约束动作序列，提升机器人学习的鲁棒性与稳定性，在仿真和真实实验中成功率分别提升29.73%和39.4%。
摘要：
行为克隆（BC）是机器人学、自动驾驶等领域中一种流行的监督式模仿学习方法，通过直接模仿专家演示可以学习复杂技能。尽管其发展迅速，但仍受限于有限的视野，传感器和关节噪声的累积会导致复合误差。在本文中，我们受神经科学家启发，引入了几何和历史约束的行为克隆（GHCBC），以主要考虑高层状态信息。其中，几何约束行为克隆用于在几何上约束姿态预测，而历史约束行为克隆用于在时间上约束动作序列。这两类约束之间的协同作用增强了行为克隆在鲁棒性和稳定性方面的性能。综合实验结果表明，与最先进的行为克隆方法相比，在仿真中成功率平均提高了29.73%，在真实机器人实验中平均提高了39.4%，特别是在长期操作场景中，表明GHCBC在机器人学习方面具有巨大潜力。
英文摘要：
Behavior cloning (BC) is a popular supervised imitation learning method in the societies of robotics, autonomous driving, etc., wherein complex skills can be learned by direct imitation from expert demonstrations. Despite its rapid development, it is still affected by limited field of view where accumulation of sensors and joint noise bring compounding errors. In this paper, we introduced geometrically and historically constrained behavior cloning (GHCBC) to dominantly consider high-level state information inspired by neuroscientists, wherein the geometrically constrained behavior cloning were used to geometrically constrain predicting poses, and the historically constrained behavior cloning were utilized to temporally constrain action sequences. The synergy between these two types of constrains enhanced the BC performance in terms of robustness and stability. Comprehensive experimental results showed that success rates were improved by 29.73% in simulation and 39.4% in real robot experiments in average, respectively, compared to state-of-the-art BC method, especially in long-term operational scenes, indicating great potential of using the GHCBC for robotic learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
